mlsanityData to 14 Oct 2026

GraphDOP: Towards skilful data-driven medium-range weather forecasts learnt and initialised directly from observations

arXiv 2412.15687 ↗First posted 20 Dec 2024physics.ao-ph cs.LGcited by 27

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Abstract (arXiv)

We introduce GraphDOP, a new data-driven, end-to-end forecast system developed at the European Centre for Medium-Range Weather Forecasts (ECMWF) that is trained and initialised exclusively from Earth System observations, with no physics-based (re)analysis inputs or feedbacks. GraphDOP learns the correlations between observed quantities - such as brightness temperatures from polar orbiters and geostationary satellites - and geophysical quantities of interest (that are measured by conventional observations), to form a coherent latent representation of Earth System state dynamics and physical processes, and is capable of producing skilful predictions of relevant weather parameters up to five days into the future.

Key facts. Cited by 27 papers in the map (data through 14 Oct 2026). First posted on arXiv 20 Dec 2024.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 9; 2026: 18.

27 papers.

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  1. 2025Appa: Bending Weather Dynamics with Latent Diffusion Models for Global Data Assimilationcited by 20
  2. 2025Deep Learning and Foundation Models for Weather Prediction: A Surveycited by 16
  3. 2025Observation-guided Interpolation Using Graph Neural Networks for High-Resolution Nowcasting in Switzerlandcited by 4
  4. 2025Huracan: A skillful end-to-end data-driven system for ensemble data assimilation and weather predictioncited by 2
  5. 2025Learning Coupled Earth System Dynamics with GraphDOPcited by 2
  6. 2026Towards accurate extreme event likelihoods from diffusion model climate emulatorscited by 2
  7. 2026Skillful high-resolution weather forecasting independent of physical modelscited by 2
  8. 2025XiChen: A global weather observation-to-forecast machine learning system via four-dimensional variational gradient-guided flexible assimilationcited by 1
  9. 2025Learning more physically realistic dynamics in machine-learning based weather forecasting with latent-space constraintscited by 1
  10. 2025DAWP: A framework for global observation forecasting via Data Assimilation and Weather Prediction in satellite observation spacecited by 1
  11. 2026Probabilistic Transformers for Joint Modeling of Global Weather Dynamics and Decision-Centric Variablescited by 1
  12. 2026Operator Learning for Smoothing and Forecastingcited by 1
15 more
  1. 2026Earth-o1: A Grid-free Observation-native Atmospheric World Modelcited by 1
  2. 2026Towards a Foundation Model for the Martian Atmospherecited by 1
  3. 2025On the use of Graphs for Satellite Image Time Seriescited by 0
  4. 2026FuXiWeather2: Learning accurate atmospheric state estimation for operational global weather forecastingcited by 0
  5. 2026Toward Artificial Intelligence Enabled Earth System Couplingcited by 0
  6. 2026AIFS-COMPO: A Global Data-Driven Atmospheric Composition Forecasting Systemcited by 0
  7. 2026Earth Science Foundation Models: From Perception to Reasoning and Discoverycited by 0
  8. 2026Accurate, Efficient, and Explainable Deep Learning Approaches for Environmental Science Problemscited by 0
  9. 2026Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matchingcited by 0
  10. 2026Timestep-Conditioned Transformers for Global Weather Forecastingcited by 0
  11. 2026Earth observation embeddings are effective sub-grid descriptors for probabilistic weather downscalingcited by 0
  12. 2026OceanDepths: A Global Dataset of Paired Subsurface and Surface Ocean Observationscited by 0
  13. 2026Uncertainty-Aware End-to-End AI Weather Forecasting: Disentangling Observation and Model Contributionscited by 0
  14. 2026The interface of data assimilation and machine learningcited by 0
  15. 2026Artificial intelligence pathways from weather to climatecited by 0